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Using spatial data support for reducing uncertainty in geospatial applications / T. Hong in Geoinformatica, vol 18 n° 1 (January 2014)  

Public [article]  inGeoinformatica > vol 18 n° 1 (January 2014) . - pp 63 - 92 Titre : Using spatial data support for reducing uncertainty in geospatial applications Type de document : Article/Communication Auteurs : T. Hong, Auteur ; K. Hart, Auteur ; Leen-Kiat Soh, Auteur ; Ashok Samal, Auteur Année de publication : 2014 Article en page(s) : pp 63 - 92 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] exploration de données géographiques [Termes IGN] incertitude des données [Termes IGN] Nebraska (Etats-Unis) [Termes IGN] série temporelle Résumé : (Auteur) Widespread use of GPS devices and ubiquity of remotely sensed geospatial images along with cheap storage devices have resulted in vast amounts of digital data. More recently, with the advent of wireless technology, a large number of sensor networks have been deployed to monitor many human, biological and natural processes. This poses a challenge in many data rich application domains now: how to best choose the datasets to solve specific problems? In particular, some of the datasets may be redundant and their inclusion in analysis may not only be time consuming, but also lead to erroneous conclusions. On the other hand, excluding some of the datasets hastily might skew the observations drawn. We propose the concept of data support as the basis for efficient, cost-effective and intelligent use of geospatial data in order to reduce uncertainty in the analysis and consequently in the results. Data support is defined as the process of determining the information utility of a data source to help decide which one to include or exclude to improve cost-effectiveness in existing data analysis. In this paper we use mutual information—a concept popular in information theory as a measure to compute information gain or loss between two datasets—as the basis of computing data support. The flexibility and effectiveness of the approach are demonstrated using an application in the hydrological analysis domain, specifically, watersheds in the state of Nebraska. Numéro de notice : A2014-028 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-013-0177-z Date de publication en ligne : 12/06/2013 En ligne : https://doi.org/10.1007/s10707-013-0177-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32933 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
057-2014011	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Delineation of impervious surface from multispectral imagery and lidar incorporating knowledge based expert system rules / K. Germaine in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 1 (January 2011)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 77 n° 1 (January 2011) . - pp 75 - 85 Titre : Delineation of impervious surface from multispectral imagery and lidar incorporating knowledge based expert system rules Type de document : Article/Communication Auteurs : K. Germaine, Auteur ; M.C. Hung, Auteur Année de publication : 2011 Article en page(s) : pp 75 - 85 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification ISODATA [Termes IGN] données lidar [Termes IGN] image multibande [Termes IGN] Nebraska (Etats-Unis) [Termes IGN] précision de la classification [Termes IGN] surface imperméable [Termes IGN] système à base de connaissances [Termes IGN] système expert Résumé : (Auteur) An attempt to delineate impervious surfaces in the City of Scottsbluff, Nebraska, was made using multispectral high spatial resolution imagery and lidar data. An isodata classification was performed and results aggregated into two parent classes, impervious and pervious. The ISODATA classification yielded an overall accuracy of 91.0 percent with a Kappa of 82.0 percent. A Knowledge Based Expert System (kbes) set of rules was designed incorporating the imagery classification with lidar data to derive two models, Cover Height and Cover Slope, to provide critical information not available from multispectral imagery. The rules were applied to the initial isodata classification to improve the classification accuracy to an overall accuracy of 94.0 percent with a Kappa of 87.9 percent. In this study, it was shown that lidar holds promise for improving the accuracy of impervious surface measurement, as well as the potential identification and measurement of other significant planimetric features such as buildings and trees. Numéro de notice : A2011-003 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.77.1.75 En ligne : https://doi.org/10.14358/PERS.77.1.75 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30785 [article]

A GIS-based spatial pattern analysis model for eco-region mapping and characterization / Y. Zhou in International journal of geographical information science IJGIS, vol 17 n° 5 (july - August 2003)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 17 n° 5 (july - August 2003) . - pp 445 - 462 Titre : A GIS-based spatial pattern analysis model for eco-region mapping and characterization Type de document : Article/Communication Auteurs : Y. Zhou, Auteur ; S. Narumalani, Auteur ; W.J. Waltman, Auteur ; et al., Auteur Année de publication : 2003 Article en page(s) : pp 445 - 462 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] analyse spatiale [Termes IGN] carte thématique [Termes IGN] délimitation [Termes IGN] écosystème [Termes IGN] Nebraska (Etats-Unis) [Termes IGN] polygone [Termes IGN] reconnaissance de formes Résumé : (Auteur) Growing concerns about global climate change, biodiversity maintenance, natural resources conservation, and long-term ecosystem sustainability have been responsible for the transformation of traditional single resource management approaches into integrated ecosystem management models. Eco-regions are large ecosystems of regional extent that contain smaller ecosystems of similar response potential and resource production capabilities. They can be used as a geographical framework for organizing and reporting resource information, setting bioecological recovery criteria, extrapolating site-level management, and monitoring global change. The objective of this research is to develop a quantitative, multivariate regionalization model that is capable of delineating eco-regions at multiple levels from remotely sensed information and other environmental and natural resources spatial data. The Spatial Pattern Analysis Model developed in this study uses a region-growing algorithm to generate spatially contiguous regions from primitive polygonal land units. The algorithm merges the most similar pair of neighbouring units at each iteration, based on satisfying certain similarity criteria until all units are grouped into one. This model was utilized to develop an eco-region map of Nebraska with three hierarchical levels. In the mapping process, the STATSGO data set was used to build the primitive map units. Environmental parameters included in the model were multi-temporal AVHRR data, soil rooting depth, organic matter content, available water capacity, and long-term annual averages of water balance and growing degree day totals. Numéro de notice : A2003-144 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1080/1365881031000086983 En ligne : https://doi.org/10.1080/1365881031000086983 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22440 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-03051	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-03052	RAB	Revue	Centre de documentation	En réserve L003	Disponible